def run_iteration(self, task, c, fpop, xb, fxb, ki, *args, **dparams):
r"""Core function of EvolutionStrategyMpL algorithm.
Args:
task (Task): Optimization task.
c (numpy.ndarray): Current population.
fpop (numpy.ndarray): Current populations fitness/function values.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best individuals fitness/function value.
ki (int): Number of successful mutations.
args (list): Additional arguments.
dparams (dict): Additional keyword arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, list, dict]:
1. New population.
2. New populations function/fitness values.
3. New global best solution.
4. New global best solutions fitness/objective value.
5. Additional arguments.
6. Additional keyword arguments:
* ki (int): Number of successful mutations.
"""
if task.Iters % self.k == 0: _, ki = self.update_rho(c, ki), 0
cn = objects2array([
IndividualES(x=self.mutate_rand(c, task), task=task, rnd=self.Rand)
for _ in range(self.lam)
])
cn = np.append(cn, c)
cn = objects2array(
[cn[i] for i in np.argsort([i.f for i in cn])[:self.mu]])
ki += self.change_count(c, cn)
fcn = np.asarray([x.f for x in cn])
xb, fxb = self.get_best(cn, fcn, xb, fxb)
return cn, fcn, xb, fxb, args, {'ki': ki}
def run_iteration(self, task, caravan, fcaravan, cb, fcb, *args, **dparams): r"""Core function of Camel Algorithm. Args: task (Task): Optimization task. caravan (numpy.ndarray[Camel]): Current population of Camels. fcaravan (numpy.ndarray[float]): Current population fitness/function values. cb (Camel): Current best Camel. fcb (float): Current best Camel fitness/function value. args (list): Additional arguments. dparams (dict): Additional keyword arguments. Returns: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, list, dict]: 1. New population. 2. New population function/fitness value. 3. New global best solution. 4. New global best fitness/objective value. 5. Additional arguments. 6. Additional keyword arguments. """ ncaravan = objects2array([self.walk(c, cb, task) for c in caravan]) ncaravan = objects2array([self.oasis(c, self.rand(), self.alpha) for c in ncaravan]) ncaravan = objects2array([self.life_cycle(c, self.mu, task) for c in ncaravan]) fncaravan = np.asarray([c.f for c in ncaravan]) cb, fcb = self.get_best(ncaravan, fncaravan, cb, fcb) return ncaravan, fncaravan, cb, fcb, args, dparams
def run_iteration(self, task, c, fpop, xb, fxb, *args, **dparams):
r"""Core function of EvolutionStrategyML algorithm.
Args:
task (Task): Optimization task.
c (numpy.ndarray): Current population.
fpop (numpy.ndarray): Current population fitness/function values.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best individuals fitness/function value.
args (list): Additional arguments.
dparams (dict): Additional keyword arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, list, dict]:
1. New population.
2. New populations fitness/function values.
3. New global best solution.
4. New global best solutions fitness/objective value.
5. Additional arguments.
6. Additional keyword arguments.
"""
cn = objects2array([
IndividualES(x=self.mutate_rand(c, task),
task=task,
rand=self.Rand) for _ in range(self.lam)
])
c = self.new_pop(cn)
fc = np.asarray([x.f for x in c])
xb, fxb = self.get_best(c, fc, xb, fxb)
return c, fc, xb, fxb, args, dparams
def run_iteration(self, task, c, fpop, xb, fxb, ki, *args, **dparams):
r"""Core function of EvolutionStrategy(1+1) algorithm.
Args:
task (Task): Optimization task.
pop (Individual): Current position.
fpop (float): Current position function/fitness value.
xb (Individual): Global best position.
fxb (float): Global best function/fitness value.
ki (int): Number of successful updates before rho update.
args (list): Additional arguments.
dparams (dict): Additional keyword arguments.
Returns:
Tuple[Individual, float, Individual, float, list, dict]:
1, Initialized individual.
2, Initialized individual fitness/function value.
3. New global best solution.
4. New global best solutions fitness/objective value.
5. Additional arguments.
6. Additional keyword arguments:
* ki (int): Number of successful rho update.
"""
if task.Iters % self.k == 0: c.rho, ki = self.update_rho(c.rho, ki), 0
cn = objects2array([
task.repair(self.mutate(c.x, c.rho), self.Rand)
for _i in range(self.mu)
])
cn_f = np.asarray([task.eval(cn[i]) for i in range(len(cn))])
ib = np.argmin(cn_f)
if cn_f[ib] < c.f:
c.x, c.f, ki = cn[ib], cn_f[ib], ki + 1
if cn_f[ib] < fxb:
xb, fxb = self.get_best(cn[ib], cn_f[ib], xb, fxb)
return c, c.f, xb, fxb, args, {'ki': ki}
def new_pop(self, pop):
r"""Return new population.
Args:
pop (numpy.ndarray): Current population.
Returns:
numpy.ndarray: New population.
"""
pop_s = np.argsort([i.f for i in pop])
if self.mu < self.lam:
return objects2array([pop[i] for i in pop_s[:self.mu]])
npop = list()
for i in range(int(ceil(float(self.mu) / self.lam))):
npop.extend(
pop[:self.lam if (self.mu -
i * self.lam) >= self.lam else self.mu -
i * self.lam])
return objects2array(npop)
def init_pop(self, task, n, rnd, itype, *args, **kwargs): r"""Initialize starting population. Args: task (Task): Optimization task. n (int): Number of camels in population. rnd (mtrand.RandomState): Random number generator. itype (Individual): Individual type. wargs (list): Additional arguments. kwargs (dict): Additional keyword arguments. Returns: Tuple[numpy.ndarray, numpy.ndarray, list, dict]: 1. Initialize population of camels. 2. Initialized populations function/fitness values. 3. Additional arguments. 4. Additional keyword arguments. """ caravan = objects2array([itype(E_init=self.E_init, S_init=self.S_init, task=task, rnd=rnd, e=True) for _ in range(n)]) return caravan, np.asarray([c.f for c in caravan]), args, kwargs
def default_individual_init(task, n, rnd=rand, itype=None, *args, **kwargs):
r"""Initialize `n` individuals of type `itype`.
Args:
task (Task): Optimization task.
n (int): Number of individuals in population.
rnd (Optional[rand.RandomState]): Random number generator.
itype (Optional[Individual]): Class of individual in population.
args (list): Additional arguments.
kwargs (dict): Additional keyword arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]:
1. Initialized individuals.
2. Initialized individuals function/fitness values.
3. Additional arguments.
4. Additional keyword arguments.
"""
pop = objects2array([itype(task=task, rnd=rnd, e=True) for _ in range(n)])
return pop, np.asarray([x.f for x in pop]), args, kwargs
def init_school(self, task):
"""Initialize fish school with uniform distribution."""
curr_step_individual = self.step_individual_init * (task.Upper -
task.Lower)
curr_step_volitive = self.step_volitive_init * (task.Upper -
task.Lower)
curr_weight_school = 0.0
school = []
positions = self.generate_uniform_coordinates(task)
for idx in range(self.NP):
fish = self.init_fish(positions[idx], task)
school.append(fish)
curr_weight_school += fish.weight
prev_weight_school = curr_weight_school
return curr_step_individual, curr_step_volitive, curr_weight_school, prev_weight_school, objects2array(
school)